Multi-view data, typically associated with multiple modalities or multiple types of features, often exists in real-world scenarios. State-of-the-art multi-view learning methods achieve tremendous success across a wide range of real-world applications. However, this success typically relies on complex models [1]}, [2]}, [3]}, [4]}, [5]}, which tend to integrate multi-view information with deep neural networks. Although these models can provide accurate classification results, they are usually vulnerable to yield unreliable predictions, particularly when presented with views that are not well-represented (e.g., information from abnormal sensors). Consequently, their deployment in safety-critical applications (e.g., computer-aided diagnosis or autonomous driving) is limited. This has inspired us to introduce a new paradigm for multi-view classification to produce trusted decisions.
